Summary

Using the wide range of tools and libraries available for working with
geospatial data, it is now possible to transport geospatial data from a
database to a web-interface in only a few lines of code. In this
tutorial, we explore some of these libraries and work through examples
which showcase the power of Python for geospatial data.

Description

Tools and libraries for working with geospatial data in Python are
currently undergoing rapid development and expansion. Libraries such as
shapely, fiona, rasterio, geopandas, and others now provide Pythonic
ways of reading, writing, editing, and manipulating geographic data. In
this tutorial, participants will be exposed to a number of new and
legacy geospatial libraries in Python, with a focus on simple and rapid
interaction with geospatial data.

We will utilize Python to interact with geographic data from a database
to a web interface, all the while showcasing how Python can be used to
access data from online resources, query spatially enabled databases,
perform coordinate transformations and geoprocessing functions, and
export geospatial data to web-enabled formats for visualizing and
sharing with others. Time permitting, we will also briefly explore
Python plugin development for the QGIS Desktop GIS environment.

This tutorial should be accessible to anyone who has basic Python
knowledge (though familiarity with Pandas, NumPy, matplotlib, etc. will
be helpful) as well as familiarity with IPython Notebook. We will take
some time at the start of the tutorial to go over installation
strategies for geospatial libraries (GDAL/OGR, Proj.4, GEOS) and their
Python bindings (Shapely, Fiona, GeoPandas) on Windows, Mac, and Linux.
Some knowledge of geospatial concepts such as map projections and GIS
data formats will also be helpful.